{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "874840c2-fafa-4e8a-b6a6-c8ba5176a6e2",
   "metadata": {},
   "source": [
    "# Decision-making for the outdoor AMR based on Random Forest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ebb3dbc7-be1b-4dd8-8ec7-d4066ea6eca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# runing time\n",
    "import time\n",
    "start = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f390d773-4ca6-4657-a356-7b18a94d6b36",
   "metadata": {},
   "source": [
    "# 0. Code to get started"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06998cfa-c17f-470c-8b75-7366e61d0363",
   "metadata": {},
   "source": [
    "## Global flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eea3453e-83c7-4bc2-a241-2f0feb452986",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure if the simulator should show pop-up windows\n",
    "# CHANGE IF NEEDED\n",
    "show_policy = True\n",
    "collect_demo = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b902a3-8a78-49a2-8e34-2e1cfbfc152b",
   "metadata": {},
   "source": [
    "## Load default modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d1ffa96-8982-4885-93a5-ef76f4e3fcf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "import cv2 # you are allowed to use functions from cv2\n",
    "import glob\n",
    "\n",
    "# import the simulation environment\n",
    "import wallgap_ro47002\n",
    "from wallgap_ro47002 import WallGap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe1e1c3a-102e-4169-9d5e-34fcd1193017",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (this cell is included for debugging, it makes it easy to reload wallgap_ro47002.py without restarting the kernel)\n",
    "import importlib\n",
    "importlib.reload(wallgap_ro47002)\n",
    "from wallgap_ro47002 import WallGap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0a9ee0c8-aaa8-4b88-a9e9-5953459f48f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time_id: 0\n",
      "time_id: 1\n",
      "time_id: 2\n",
      "time_id: 3\n"
     ]
    }
   ],
   "source": [
    "# creating environments for the 4 environments with rendering\n",
    "envs = []\n",
    " \n",
    "for n in range(4): \n",
    "    envs.append(WallGap(time_id=n, max_episode_time_step=1000, rendering=True))\n",
    "    \n",
    "# N.B.: on some computer you might get a warning \"Cannot change thread mode after it is set\"\n",
    "# when you run this cell the first time. It is safe to ignore this warning, the notebook should still work properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "49c1f03b-212b-4b24-8ce4-c481b2ee055c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time_id: 0\n",
      "time_id: 1\n",
      "time_id: 2\n",
      "time_id: 3\n"
     ]
    }
   ],
   "source": [
    "# creating environments for the 4 environments without rendering\n",
    "envs_norender = []\n",
    " \n",
    "for n in range(4): \n",
    "    envs_norender.append(WallGap(time_id=n, max_episode_time_step=1000, rendering=False)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "92c0f82f-f24c-4632-a026-672e03cfd76c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f2e586b8d9746a09096b02280f1b125",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='time_id', max=3), Output()), _dom_classes=('widget-inter…"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipywidgets\n",
    "import pyglet\n",
    "%matplotlib inline\n",
    "# Show screenshot of a sampled environment\n",
    "def plot_time_example(time_id):\n",
    "    env = envs_norender[time_id]\n",
    "    env.reset(seed=1)\n",
    "    plt.imshow(env.render())\n",
    "    env.render()\n",
    "    plt.title(f'Time {time_id}')\n",
    "    \n",
    "\n",
    "ipywidgets.interactive(plot_time_example, time_id=(0,3), continuous_update=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ca849a-41e5-4fed-8f50-e4c8cc67ea77",
   "metadata": {},
   "source": [
    "## Loading pre-recorded human demonstrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "004346d2-a52f-4b52-9a6f-fcd847b711a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 66 demonstrations\n",
      "Loaded 80 samples from demonstrations\\demo-0-20241009_131701.pickle ...\n",
      "Loaded 77 samples from demonstrations\\demo-0-20241009_131722.pickle ...\n",
      "Loaded 51 samples from demonstrations\\demo-0-20241009_131742.pickle ...\n",
      "Loaded 73 samples from demonstrations\\demo-0-20241009_131803.pickle ...\n",
      "Loaded 26 samples from demonstrations\\demo-0-20241009_131820.pickle ...\n",
      "Loaded 74 samples from demonstrations\\demo-0-20241009_131840.pickle ...\n",
      "Loaded 82 samples from demonstrations\\demo-0-20241009_131901.pickle ...\n",
      "Loaded 53 samples from demonstrations\\demo-0-20241009_131919.pickle ...\n",
      "Loaded 96 samples from demonstrations\\demo-0-20241009_131941.pickle ...\n",
      "Loaded 67 samples from demonstrations\\demo-0-20241009_132004.pickle ...\n",
      "Loaded 75 samples from demonstrations\\demo-0-20241009_132028.pickle ...\n",
      "Loaded 59 samples from demonstrations\\demo-0-20241009_132046.pickle ...\n",
      "Loaded 53 samples from demonstrations\\demo-0-20241009_132106.pickle ...\n",
      "Loaded 78 samples from demonstrations\\demo-0-20241009_132127.pickle ...\n",
      "Loaded 18 samples from demonstrations\\demo-0-20241009_132144.pickle ...\n",
      "Loaded 51 samples from demonstrations\\demo-0-20241009_132737.pickle ...\n",
      "Loaded 65 samples from demonstrations\\demo-0-20241009_132757.pickle ...\n",
      "Loaded 96 samples from demonstrations\\demo-0-20241009_132821.pickle ...\n",
      "Loaded 38 samples from demonstrations\\demo-0-20241009_132840.pickle ...\n",
      "Loaded 45 samples from demonstrations\\demo-0-20241009_132859.pickle ...\n",
      "Loaded 34 samples from demonstrations\\demo-0-20241009_132920.pickle ...\n",
      "Loaded 54 samples from demonstrations\\demo-0-20241009_133014.pickle ...\n",
      "Loaded 60 samples from demonstrations\\demo-0-20241009_133037.pickle ...\n",
      "Loaded 35 samples from demonstrations\\demo-1-20241009_131856.pickle ...\n",
      "Loaded 59 samples from demonstrations\\demo-1-20241009_131923.pickle ...\n",
      "Loaded 62 samples from demonstrations\\demo-1-20241009_131941.pickle ...\n",
      "Loaded 18 samples from demonstrations\\demo-1-20241009_131953.pickle ...\n",
      "Loaded 76 samples from demonstrations\\demo-1-20241009_132025.pickle ...\n",
      "Loaded 54 samples from demonstrations\\demo-1-20241009_132040.pickle ...\n",
      "Loaded 77 samples from demonstrations\\demo-1-20241009_132101.pickle ...\n",
      "Loaded 38 samples from demonstrations\\demo-1-20241009_132113.pickle ...\n",
      "Loaded 62 samples from demonstrations\\demo-1-20241009_132135.pickle ...\n",
      "Loaded 37 samples from demonstrations\\demo-1-20241009_132149.pickle ...\n",
      "Loaded 62 samples from demonstrations\\demo-1-20241009_132723.pickle ...\n",
      "Loaded 68 samples from demonstrations\\demo-1-20241009_132752.pickle ...\n",
      "Loaded 61 samples from demonstrations\\demo-1-20241009_132816.pickle ...\n",
      "Loaded 61 samples from demonstrations\\demo-1-20241009_132834.pickle ...\n",
      "Loaded 45 samples from demonstrations\\demo-1-20241009_132855.pickle ...\n",
      "Loaded 62 samples from demonstrations\\demo-1-20241009_132911.pickle ...\n",
      "Loaded 37 samples from demonstrations\\demo-1-20241009_132922.pickle ...\n",
      "Loaded 75 samples from demonstrations\\demo-1-20241009_132942.pickle ...\n",
      "Loaded 45 samples from demonstrations\\demo-1-20241009_132957.pickle ...\n",
      "Loaded 64 samples from demonstrations\\demo-1-20241009_133017.pickle ...\n",
      "Loaded 57 samples from demonstrations\\demo-2-20241009_133042.pickle ...\n",
      "Loaded 45 samples from demonstrations\\demo-2-20241009_133056.pickle ...\n",
      "Loaded 47 samples from demonstrations\\demo-2-20241009_133109.pickle ...\n",
      "Loaded 68 samples from demonstrations\\demo-2-20241009_133113.pickle ...\n",
      "Loaded 76 samples from demonstrations\\demo-2-20241009_133130.pickle ...\n",
      "Loaded 55 samples from demonstrations\\demo-2-20241009_133136.pickle ...\n",
      "Loaded 45 samples from demonstrations\\demo-2-20241009_133201.pickle ...\n",
      "Loaded 34 samples from demonstrations\\demo-2-20241009_133211.pickle ...\n",
      "Loaded 61 samples from demonstrations\\demo-2-20241009_133219.pickle ...\n",
      "Loaded 53 samples from demonstrations\\demo-2-20241009_133229.pickle ...\n",
      "Loaded 48 samples from demonstrations\\demo-2-20241009_133248.pickle ...\n",
      "Loaded 64 samples from demonstrations\\demo-2-20241009_133250.pickle ...\n",
      "Loaded 58 samples from demonstrations\\demo-2-20241009_133308.pickle ...\n",
      "Loaded 64 samples from demonstrations\\demo-2-20241009_133315.pickle ...\n",
      "Loaded 81 samples from demonstrations\\demo-2-20241009_133331.pickle ...\n",
      "Loaded 54 samples from demonstrations\\demo-2-20241009_133350.pickle ...\n",
      "Loaded 55 samples from demonstrations\\demo-2-20241009_133445.pickle ...\n",
      "Loaded 82 samples from demonstrations\\demo-2-20241009_133451.pickle ...\n",
      "Loaded 55 samples from demonstrations\\demo-2-20241009_133504.pickle ...\n",
      "Loaded 72 samples from demonstrations\\demo-2-20241009_133513.pickle ...\n",
      "Loaded 75 samples from demonstrations\\demo-2-20241009_133539.pickle ...\n",
      "Loaded 71 samples from demonstrations\\demo-2-20241009_133600.pickle ...\n",
      "Loaded 56 samples from demonstrations\\demo-2-20241009_133627.pickle ...\n"
     ]
    }
   ],
   "source": [
    "# Look for all the demonstration pickle files in the demonstrations/ directory.\n",
    "#  The originally provided demo files are called: demo-[room_id]-[datetime].pickle\n",
    "#  Any demo files you record yourself are called: demostud-[room_id]-[datetime].pickle\n",
    "\n",
    "# CHANGE THIS IF NEEDED: select the pickle file pattern to match ...\n",
    "\n",
    "#DEMO_FILEPATTERN = 'demo-*-*.pickle'      # only use ORIGINALLY provided demo files\n",
    "#DEMO_FILEPATTERN = 'demostud-*-*.pickle'  # only use YOUR own collected demo files\n",
    "DEMO_FILEPATTERN = 'demo*-*.pickle'        # use ALL available demo files\n",
    "\n",
    "# find the relevant filenames\n",
    "filenames = glob.glob(f'demonstrations/{DEMO_FILEPATTERN}')\n",
    "filenames.sort() # ensure the order is well-defined\n",
    "print(f'Found {len(filenames)} demonstrations')\n",
    "\n",
    "# in a loop, load the found pickle files\n",
    "demonstrations = []\n",
    "for filename in filenames:\n",
    "    with open(filename, 'rb') as fd:\n",
    "        demonstration = pickle.load(fd)\n",
    "        \n",
    "        actions = demonstration['actions']\n",
    "        print(f'Loaded {actions.shape[0]} samples from {filename} ...')\n",
    "        \n",
    "        demonstrations.append(demonstration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "65c0f56f-4248-42f0-b651-6db92e080e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can combine all the observations, actions, and time ids\n",
    "observations = np.concatenate([d['observations'] for d in demonstrations])\n",
    "actions = np.concatenate([d['actions'] for d in demonstrations])\n",
    "time_ids = np.concatenate([d['time'] for d in demonstrations])\n",
    "\n",
    "# pre-processing: subsample and only keep every n-th sample for efficiency\n",
    "# this can speed up training\n",
    "ss = 1\n",
    "observations = observations[::ss]\n",
    "actions = actions[::ss]\n",
    "time_ids = time_ids[::ss]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d2ebe5-9ea5-4736-b33c-6c8d30449a98",
   "metadata": {},
   "source": [
    "## Understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a332542-602d-496a-8e5f-95098e9cbb5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action of sample 1000:                     2\n",
      "Time id when sample 1000 was recorded:     0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = 1000 # sample index, should be in range [0, N-1]\n",
    "print(f'Action of sample {idx}:                    ', actions[idx])\n",
    "print(f'Time id when sample {idx} was recorded:    ', time_ids[idx])\n",
    "\n",
    "plt.imshow(observations[idx])\n",
    "plt.title(f'Observation of sample {idx}');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce114e60-193e-41f2-aeff-3bea4304dfd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ACTION_LEFT = 0  # Turn left\n",
    "ACTION_RIGHT = 1 # Turn right\n",
    "ACTION_FORWARD = 2  # Move forward"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1a4ab72-5387-4b28-ab55-20c7c1f429e3",
   "metadata": {},
   "source": [
    "## Testing your model in  the simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "102a0ed0-4774-4280-8850-78ee490a5ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# user key input handler\n",
    "from pyglet.window import key\n",
    "import time\n",
    "\n",
    "# keep track if which keys have been pressed in the popup window\n",
    "# (will be used later for the human control)\n",
    "KEY_PRESSED = {key.LEFT: False, key.RIGHT: False, key.UP: False}\n",
    "\n",
    "# define the set of all actions    \n",
    "ACTIONS = [0, 1, 2]\n",
    "ACTION_NAMES = ['turn_left', 'turn_right', 'move_forward']\n",
    "NUM_ACTIONS = 3 # number of distinct actions\n",
    "\n",
    "def run_simulation(policy, env, max_steps=500, verbose=1, record_data=False, delay=0.0, seed=-1, human_control=False):\n",
    "    \"\"\" Run robot simulation\n",
    "    Input arguments:\n",
    "    - policy        # [function] the robot's policy function\n",
    "    - env           # [instance of WallGap] the environment to simulate\n",
    "    - record_data   # [True/False] if true, return all (observation, action) pairs from the simulation\n",
    "    - max_steps     # [int] the maximum number of steps N to run the simulation\n",
    "    - delay         # [float] a time delay that can be added to make the simulation run a bit slower\n",
    "    - seed          # [int] location generation random seed - only set if >=0\n",
    "    - verbose       # [int] how much texts gets printed: 0 = none, 1 = final stats, 2 = all\n",
    "    - human_control # [True/False] if true the key presses will be passed on the policy\n",
    "    \n",
    "    Returns:\n",
    "    - rewards       # [numpy array of floats] all N rewards accumulated during the simulation\n",
    "    - observations  # [numpy array N x H x W x 3] N observations, each observation being a WxH 3-channel image\n",
    "    - actions       # [numpy array of ints] all N actions outputted by the given policy f\n",
    "    \n",
    "    Note: `observations` and `actions` are only returned if record_data=True\n",
    "    \"\"\"\n",
    "    \n",
    "    if verbose > 0:\n",
    "        print(f'Starting simulation for {max_steps} steps.')\n",
    "        if env.rendering:\n",
    "            print('*** Press ESC key in popup window to stop the simulation! ***')\n",
    "        print()\n",
    "\n",
    "    if seed>=0:\n",
    "#         env.seed(seed)\n",
    "        env.reset(seed=seed)\n",
    "    else:\n",
    "#         env.seed(int( time.time() * 1000.0 ))\n",
    "        env.reset(seed=int( time.time() * 1000.0 ))\n",
    "\n",
    "    rewards = [] # will store the accumulated rewards\n",
    "    observations = [] # will store the accumulated observations (only if record_data==True)\n",
    "    actions = [] # will store the accumulated actions outputted by policy f (only if record_data==True)\n",
    "\n",
    "    completed_steps = 0   \n",
    "    \n",
    "    \n",
    "    try:\n",
    "        # reset the simulation, and get the initial observation (robot \"sensor measurement\")\n",
    "        obs = env.reinit(max_steps, wait_for_keypress=human_control)[0]\n",
    "        \n",
    "        # main simulation loop\n",
    "        for step in range(max_steps):\n",
    "            time.sleep(delay)\n",
    "               \n",
    "            if env.rendering:\n",
    "                env.render()\n",
    "            \n",
    "            # get keyboard pressed button status from environment\n",
    "            KEY_PRESSED.update(env.key_pressed)\n",
    "\n",
    "            if env.stop_simulation: break\n",
    "            \n",
    "                \n",
    "            # ** APPLYING YOUR POLICY **\n",
    "            # execute the given policy on the observation to determine the robot's action\n",
    "            action = policy(obs)\n",
    "            \n",
    "            # sanity check: is the policy implemented correctly?\n",
    "            assert (isinstance(action, (int, np.integer))) # returned action should be a builtin or numpy integer\n",
    "            assert (action in ACTIONS) # action should be an integer 0, 1, 2 or 3\n",
    "            \n",
    "            if verbose > 1:\n",
    "                print(f'step {step}: action = {ACTION_NAMES[action]}')\n",
    "\n",
    "            if record_data:\n",
    "                # only store all the observation and action pairs during the simulation\n",
    "                #   if the record_data argument is set to True\n",
    "                observations.append(obs)\n",
    "                actions.append(action)\n",
    "\n",
    "            # execute simulation step with the given control input\n",
    "            obs, reward, environment_done, truncation, info = env.step(action)\n",
    "            completed_steps += 1\n",
    "            \n",
    "            if verbose > 1:\n",
    "                print(f'step {step}: reward = {reward}')\n",
    "\n",
    "            # collect all rewards in a list\n",
    "            rewards.append(reward)\n",
    "\n",
    "            # exit simulation when goal is reached\n",
    "            if environment_done or truncation:\n",
    "                break\n",
    "\n",
    "    finally:\n",
    "        # close the pop-up window,\n",
    "        if env.rendering:\n",
    "            env.close()\n",
    "            env.window = None\n",
    "        \n",
    "    rewards = np.array(rewards)\n",
    "    total_reward = np.sum(rewards)\n",
    "    \n",
    "    if verbose > 0:\n",
    "        print(f'total reward after {completed_steps} steps: {total_reward}')\n",
    "        print(f'average reward: {total_reward/completed_steps}')\n",
    "    \n",
    "    if record_data:\n",
    "        return rewards, np.array(observations), np.array(actions, dtype=int), truncation\n",
    "    \n",
    "    # by default, only return the rewards\n",
    "    return rewards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88aa5ffb-c2f0-4abf-a68b-62552bc8e2eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_policy(policy, env, verbose=0, num_runs=10):\n",
    "    \"\"\" Test a policy\n",
    "    Input arguments:\n",
    "    - policy        # [function] the robot's policy function\n",
    "    - env           # [instance of WallGap] the environment to simulate\n",
    "    - verbose       # [int] how much text gets printed: 0 = none, 1 = final stats, 2 = all\n",
    "    - num_runs       # [int] how many configurations to evaluate\n",
    "    \n",
    "    Returns:\n",
    "    - num_completed  # [int] number of runs in which the goal was reached\n",
    "    - avg_reward    # [float] average reward over the runs\n",
    "    \"\"\" \n",
    "    \n",
    "    num_completed = 0\n",
    "    avg_reward = 0.0\n",
    "  \n",
    "    seeds = range(0,num_runs)\n",
    "    run_count = 1\n",
    "    for seed in seeds:\n",
    "        print('Run %i / %i' % (run_count, num_runs))\n",
    "        success = False\n",
    "        run_count += 1\n",
    "        rs = run_simulation(\n",
    "            policy,\n",
    "            env,\n",
    "            record_data=False,\n",
    "            verbose=verbose, \n",
    "            max_steps=300, \n",
    "            delay=0.0,\n",
    "            seed=seed)\n",
    "\n",
    "        avg_reward += np.mean(rs)\n",
    "        if np.sum(rs) > 0:\n",
    "            num_completed += 1\n",
    "            success = True\n",
    "            \n",
    "        print('Success:', success)\n",
    "    \n",
    "    avg_reward /= float(num_runs)\n",
    "    \n",
    "    print()\n",
    "    print(f'average reward: {avg_reward}')\n",
    "    print(f'reached target: {num_completed} / {num_runs}')\n",
    "    \n",
    "    return num_completed, avg_reward"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6142913-80b1-4792-bba4-05243e476af7",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 1. Explore & Inspect the Data\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d54ba3c-70ec-4104-98f8-4b9b08f78d95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 66 demonstrations\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1fe2d4945e0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add code and markdown cells\n",
    "DEMO_FILEPATTERN = 'demo*-*.pickle'        # use ALL available demo files\n",
    "\n",
    "# find the relevant filenames\n",
    "filenames = glob.glob(f'demonstrations/{DEMO_FILEPATTERN}')\n",
    "filenames.sort() # ensure the order is well-defined\n",
    "print(f'Found {len(filenames)} demonstrations')\n",
    "\n",
    "# in a loop, load the found pickle files\n",
    "demonstrations = []\n",
    "for filename in filenames:\n",
    "    with open(filename, 'rb') as fd:\n",
    "        demonstration = pickle.load(fd)\n",
    "        \n",
    "        actions = demonstration['actions']\n",
    "        #print(f'Loaded {actions.shape[0]} samples from {filename} ...')\n",
    "        \n",
    "        demonstrations.append(demonstration)\n",
    "\n",
    "observations = np.concatenate([d['observations'] for d in demonstrations])\n",
    "actions = np.concatenate([d['actions'] for d in demonstrations])\n",
    "time_ids = np.concatenate([d['time'] for d in demonstrations])\n",
    "\n",
    "# pre-processing: subsample and only keep every n-th sample for efficiency\n",
    "# this can speed up training\n",
    "ss = 1\n",
    "observations = observations[::ss]\n",
    "actions = actions[::ss]\n",
    "time_ids = time_ids[::ss]\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(3,3,1)\n",
    "plt.imshow(observations[0])\n",
    "plt.subplot(3,3,2)\n",
    "plt.imshow(observations[1])\n",
    "plt.subplot(3,3,3)\n",
    "plt.imshow(observations[2])\n",
    "\n",
    "plt.subplot(3,3,4)\n",
    "plt.imshow(observations[1405])\n",
    "plt.subplot(3,3,5)\n",
    "plt.imshow(observations[1406])\n",
    "plt.subplot(3,3,6)\n",
    "plt.imshow(observations[1407])\n",
    "\n",
    "plt.subplot(3,3,7)\n",
    "plt.imshow(observations[2503])\n",
    "plt.subplot(3,3,8)\n",
    "plt.imshow(observations[2504])\n",
    "plt.subplot(3,3,9)\n",
    "plt.imshow(observations[2505])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60458bf-bc7a-4271-83fb-89c39c2f8250",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 2. Prepare the Data and Evaluate Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "94f61f1b-45f3-4f04-a373-412cf6341915",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add code and markdown cells\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import completeness_score\n",
    "\n",
    "def feat_extract_clust(image):\n",
    "    hist_r = cv2.calcHist([image], [0], None, [256], [0, 256])\n",
    "    hist_g = cv2.calcHist([image], [1], None, [256], [0, 256])\n",
    "    hist_b = cv2.calcHist([image], [2], None, [256], [0, 256])\n",
    "    hist = np.concatenate((hist_r, hist_g, hist_b)) \n",
    "    \n",
    "    return hist.flatten()\n",
    "\n",
    "features = []\n",
    "for observation in observations:\n",
    "    feature = feat_extract_clust(observation)\n",
    "    features.append(feature)\n",
    "\n",
    "features = np.array(features)\n",
    "#print(features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a999bb94-d432-41ef-aea0-a211d4cb1da4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Conding\\Anaconda3\\envs\\homl3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Completeness Score of the cluster is: 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import completeness_score\n",
    "n_clusters = 3\n",
    "kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
    "cluster_labels = kmeans.fit_predict(features)\n",
    "\n",
    "completeness = completeness_score(time_ids, cluster_labels)\n",
    "print(f\"The Completeness Score of the cluster is: {completeness}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e731ab88-4ed9-40bf-9384-618c8ed6a384",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feat_extract(image, canny_lowb=80, canny_highb=200, dilate_kernal = 5, Edge_Weight=0.3):\n",
    "    \"\"\"\n",
    "    Extracts features from an image by combining edge detection and color filtering for red objects.\n",
    "    \n",
    "    Parameters:\n",
    "    - image (numpy array): The input RGB image.\n",
    "    - canny_lowb (int): The lower threshold for Canny edge detection.\n",
    "    - canny_highb (int): The upper threshold for Canny edge detection.\n",
    "    - dilate_kernal (int): The size of the kernel for dilating edges.\n",
    "    - Edge_Weight (float): The weight for combining edge and color features.\n",
    "    \n",
    "    Returns:\n",
    "    - flattened_features (numpy array): The flattened feature map after combining edges and red color mask, followed by max pooling.\n",
    "    \"\"\"\n",
    "    \n",
    "    edges = cv2.Canny(image, canny_lowb, canny_highb)\n",
    "\n",
    "    kernel = np.ones((dilate_kernal, dilate_kernal), np.uint8)\n",
    "\n",
    "    dilated_edge = cv2.dilate(edges, kernel, iterations=1)\n",
    "\n",
    "    hsv_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n",
    "\n",
    "    lower_red1 = np.array([0, 70, 50])\n",
    "    upper_red1 = np.array([10, 255, 255])\n",
    "    lower_red2 = np.array([170, 70, 50])\n",
    "    upper_red2 = np.array([180, 255, 255])\n",
    "\n",
    "    red_mask1 = cv2.inRange(hsv_image, lower_red1, upper_red1)\n",
    "    red_mask2 = cv2.inRange(hsv_image, lower_red2, upper_red2)\n",
    "\n",
    "    red_mask = cv2.bitwise_or(red_mask1, red_mask2)\n",
    "\n",
    "    combined = cv2.addWeighted(dilated_edge, Edge_Weight, red_mask, 1-Edge_Weight, 0)\n",
    "\n",
    "    return combined.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f5e33ba3-0d1a-4295-9888-7e935bd0fa05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 images done.\n",
      "2000 images done.\n",
      "3000 images done.\n",
      "The size of the datasets is  (3879, 4800)\n"
     ]
    }
   ],
   "source": [
    "# Create datasets\n",
    "X =[]\n",
    "count=0;\n",
    "for image in observations:\n",
    "    average_pooled = feat_extract(image)\n",
    "    X.append(average_pooled)\n",
    "    count+=1\n",
    "    if count%1000 == 0:\n",
    "        print(count, \"images done.\")\n",
    "        \n",
    "X = np.array(X)\n",
    "y = actions\n",
    "print(\"The size of the datasets is \", X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c309cb10-8488-4e5c-a628-9052d79281e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1fe71592f20>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print on single image after feature extraction\n",
    "idx = 1000\n",
    "\n",
    "plt.imshow(X[idx].reshape(60,80))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "269accba-b1f6-4c1d-8dff-869fb5e9a5e6",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "# 3. Single Time of Day Action Classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "77846d07-6d7d-48ef-b255-08b66dac6e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import pickle\n",
    "\n",
    "# X_id1 = X[np.where(time_ids==1)]\n",
    "# y_id1 = y[np.where(time_ids==1)]\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X_id1, y_id1, test_size=0.2, random_state=42)\n",
    "# X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n",
    "\n",
    "# with open('pkl/train_test_split.pkl', 'wb') as file:\n",
    "#     pickle.dump((X_train, X_test, y_train, y_test), file)\n",
    "\n",
    "# print('Training dataset size:', X_train.shape)\n",
    "# print('Evluation dataset size:', X_val.shape)\n",
    "# print('Test dataset size:', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d40fed0d-02ee-433e-b312-069c34e18435",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "with open('pkl/train_test_split.pkl', 'rb') as file:\n",
    "    X_train, X_test, y_train, y_test = pickle.load(file)\n",
    "\n",
    "# for r in [0, 42, 255]:\n",
    "#     rf_model = RandomForestClassifier(random_state=r, min_samples_leaf=4)\n",
    "#     rf_model.fit(X_train, y_train)\n",
    "    \n",
    "#     filename = 'pkl/rf_model_3-1_' + str(r) + '.pkl'\n",
    "#     with open(filename, 'wb') as f:\n",
    "#         pickle.dump(rf_model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "33a656ab-7d29-4ace-bd71-f36214e096e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "Random state is:  0\n",
      "------------------------------\n",
      "Test f1 macro: 0.50\n",
      "Test Accuracy: 0.85\n",
      "[[ 16   0  20]\n",
      " [  1   0  11]\n",
      " [  1   0 171]]\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: False\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.007335968341395068\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "------------------------------\n",
      "Random state is:  42\n",
      "------------------------------\n",
      "Test f1 macro: 0.49\n",
      "Test Accuracy: 0.85\n",
      "[[ 15   0  21]\n",
      " [  1   0  11]\n",
      " [  1   0 171]]\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.007693178813820476\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "------------------------------\n",
      "Random state is:  255\n",
      "------------------------------\n",
      "Test f1 macro: 0.51\n",
      "Test Accuracy: 0.85\n",
      "[[ 17   0  19]\n",
      " [  1   0  11]\n",
      " [  1   0 171]]\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.005883910405842172\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "CPU times: total: 49.6 s\n",
      "Wall time: 25.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import f1_score, accuracy_score\n",
    "\n",
    "def rf_policy(observation):    \n",
    "    feature_img = feat_extract(observation)\n",
    "    action = rf_model.predict(feature_img.reshape(1, -1))\n",
    "    return action[0]\n",
    "\n",
    "for r in [0, 42, 255]:\n",
    "    filename = 'pkl/rf_model_3-1_' + str(r) + '.pkl'\n",
    "\n",
    "    with open(filename, 'rb') as f:\n",
    "        rf_model = pickle.load(f)\n",
    "    \n",
    "    y_test_pred = rf_model.predict(X_test.reshape(X_test.shape[0], -1))\n",
    "    \n",
    "    test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "    test_f1_macro = f1_score(y_test, y_test_pred, average='macro')\n",
    "    print(\"------------------------------\")\n",
    "    print(\"Random state is: \", r)\n",
    "    print(\"------------------------------\")\n",
    "    print(f'Test f1 macro: {test_f1_macro:.2f}')\n",
    "    print(f'Test Accuracy: {test_accuracy:.2f}')\n",
    "    print(confusion_matrix(y_test, y_test_pred))\n",
    "\n",
    "    num_completed, avg_reward = test_policy(rf_policy, envs_norender[1])\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e7432ee6-0105-4a6f-846c-dcd3330f7c5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers, regularizers\n",
    "from keras.optimizers import Adam \n",
    "import tensorflow as tf\n",
    "\n",
    "def NN_model(pooling_size, l1, dp1, dp2, dp3, unit1, unit2, lr, weight, X_train, X_val): \n",
    "    \n",
    "    # Define the model\n",
    "    model = tf.keras.Sequential()\n",
    "    \n",
    "    # Input layer\n",
    "    model.add(layers.InputLayer(input_shape=(60, 80, 1)))\n",
    "    \n",
    "    model.add(layers.MaxPooling2D((pooling_size, pooling_size)))\n",
    "    \n",
    "    # Flattening the input\n",
    "    model.add(layers.Flatten())\n",
    "    model.add(layers.Dropout(dp1))  # Dropout with 50% rate\n",
    "    \n",
    "    # First Dense layer with L1 regularization and dropout\n",
    "    model.add(layers.Dense(unit1, \n",
    "                           activation='relu', \n",
    "                           kernel_regularizer=regularizers.l1(l1)))  # L1 regularization\n",
    "    model.add(layers.Dropout(dp2))  # Dropout with 50% rate\n",
    "    \n",
    "    # Second Dense layer with L1 regularization and dropout\n",
    "    model.add(layers.Dense(unit2, \n",
    "                           activation='relu', \n",
    "                           kernel_regularizer=regularizers.l1(l1)))  # L1 regularization\n",
    "    model.add(layers.Dropout(dp3))  # Dropout with 50% rate\n",
    "    \n",
    "    \n",
    "    # Output layer for 3-class classification (softmax activation)\n",
    "    model.add(layers.Dense(3, activation='softmax'))\n",
    "    \n",
    "    # Compile the model\n",
    "    model.compile(optimizer=Adam(learning_rate = lr), \n",
    "                  loss='sparse_categorical_crossentropy', \n",
    "                 )\n",
    "    \n",
    "    history = model.fit(X_train, y_train, epochs=50, validation_data=(X_val, y_val), class_weight=weight, verbose=0)\n",
    "\n",
    "    return model, history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "01b04f97-8325-4b58-a8c9-83124aaed6be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# num_classes = 3\n",
    "\n",
    "# for r in [0, 42, 255]:\n",
    "    \n",
    "#     np.random.seed(r)\n",
    "#     random.seed(r)\n",
    "#     tf.random.set_seed(r)\n",
    "    \n",
    "#     X_train = X_train.reshape(X_train.shape[0], 60, 80, 1) \n",
    "#     X_val = X_val.reshape(X_val.shape[0], 60, 80, 1)\n",
    "#     model, history = NN_model(4, 0.005, 0.0, 0.2, 0.2, 255, 255, 5e-3, {0: 4, 1: 1, 2: 1}, X_train, X_val)\n",
    "\n",
    "#     filename = 'pkl/nn_model_3-1_' + str(r) + '.pkl'\n",
    "#     with open(filename, 'wb') as f:\n",
    "#         pickle.dump(model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37b98814-d6ec-4000-a3ae-8df6ca3a8aa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "Random state is:  0\n",
      "------------------------------\n",
      "F1_macro:  0.44293616327782726\n",
      "accuracy:  0.7181818181818181\n",
      "[[ 28   0   8]\n",
      " [  3   0   9]\n",
      " [ 42   0 130]]\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: False\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.006207629739423218\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "------------------------------\n",
      "Random state is:  42\n",
      "------------------------------\n",
      "F1_macro:  0.6486415921574423\n",
      "accuracy:  0.8409090909090909\n",
      "[[ 25   0  11]\n",
      " [  2   3   7]\n",
      " [ 15   0 157]]\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: False\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.01091918247231622\n",
      "reached target: 8 / 10\n",
      "\n",
      "\n",
      "------------------------------\n",
      "Random state is:  255\n",
      "------------------------------\n",
      "F1_macro:  0.5436450342110719\n",
      "accuracy:  0.8409090909090909\n",
      "[[ 16   0  20]\n",
      " [  0   1  11]\n",
      " [  4   0 168]]\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: False\n",
      "\n",
      "average reward: 0.0036815692392014676\n",
      "reached target: 3 / 10\n",
      "\n",
      "\n",
      "CPU times: total: 4min 26s\n",
      "Wall time: 3min 25s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "def cnn_policy(observation):    \n",
    "    feature_img = feat_extract(observation)\n",
    "    action_prob = model.predict(feature_img.reshape(1, 60,80,1), verbose=0)\n",
    "    action = np.argmax(action_prob)\n",
    "    return action\n",
    "\n",
    "for r in [0, 42, 255]:\n",
    "    filename = 'pkl/nn_model_3-1_' + str(r) + '.pkl'\n",
    "\n",
    "    with open(filename, 'rb') as f:\n",
    "        model = pickle.load(f)\n",
    "    X_test = X_test.reshape(X_test.shape[0], 60, 80, 1)  \n",
    "    prd = model.predict(X_test, verbose=0)\n",
    "    y_test_pre = np.argmax(prd, axis=1)\n",
    "    print(\"------------------------------\")\n",
    "    print(\"Random state is: \", r)\n",
    "    print(\"------------------------------\")\n",
    "    print(\"F1_macro: \", f1_score(y_test, y_test_pre, average='macro'))\n",
    "    print(\"accuracy: \", accuracy_score(y_test, y_test_pre))\n",
    "    print(confusion_matrix(y_test, y_test_pre))\n",
    "    \n",
    "    num_completed, avg_reward = test_policy(cnn_policy, envs_norender[1])\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dbdcd794-cd8e-428c-93e9-246cbb4b7e28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# obv_id1 = observations[np.where(time_ids == 1)]\n",
    "# test_parameters = {'dilating_edges': [3, 5, 7], 'min_samples_leaf': [3, 4, 5]}\n",
    "\n",
    "# for i in range(3):\n",
    "\n",
    "#     X_t =[]\n",
    "#     count=0;\n",
    "#     for image in obv_id1:\n",
    "#         average_pooled = feat_extract(image, dilate_kernal=test_parameters['dilating_edges'][i])\n",
    "#         X_t.append(average_pooled)\n",
    "#         count+=1\n",
    "#         if count%1000 == 0:\n",
    "#             print(count, \"images done.\")\n",
    "    \n",
    "#     X_t = np.array(X_t)\n",
    "#     y_t = np.array(actions[np.where(time_ids == 1)])\n",
    "        \n",
    "#     X_train, X_test, y_train, y_test = train_test_split(X_t, y_t, test_size=0.2, random_state=42)\n",
    "#     X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n",
    "        \n",
    "    \n",
    "#     for j in range(3):\n",
    "        \n",
    "#         for r in [0, 42, 255]:\n",
    "        \n",
    "#             rf_model = RandomForestClassifier(random_state=r, min_samples_leaf=test_parameters['min_samples_leaf'][j])\n",
    "#             rf_model.fit(X_train, y_train)\n",
    "\n",
    "#             filename = 'pkl/rf_model_3-2_' + str(r) + '_' + str(test_parameters['dilating_edges'][i]) + '_'  + str(test_parameters['min_samples_leaf'][j]) + '.pkl'\n",
    "#             with open(filename, 'wb') as f:\n",
    "#                 pickle.dump(rf_model, f)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6350081b-4454-4bc6-b10f-c9db87920fbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  3\n",
      "min_samples_leaf:  3\n",
      "Random state is:  0\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: False\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.0037699450205743333\n",
      "reached target: 4 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  3\n",
      "min_samples_leaf:  3\n",
      "Random state is:  42\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.006802062436920969\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  3\n",
      "min_samples_leaf:  3\n",
      "Random state is:  255\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: False\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.005018198694598559\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  3\n",
      "min_samples_leaf:  4\n",
      "Random state is:  0\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.005023678529341271\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  3\n",
      "min_samples_leaf:  4\n",
      "Random state is:  42\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.00663287156701053\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  3\n",
      "min_samples_leaf:  4\n",
      "Random state is:  255\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.00695919898408785\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  3\n",
      "min_samples_leaf:  5\n",
      "Random state is:  0\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
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      "Success: True\n",
      "\n",
      "average reward: 0.005656375225286535\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  3\n",
      "min_samples_leaf:  5\n",
      "Random state is:  42\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
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      "Success: True\n",
      "\n",
      "average reward: 0.005741175392840853\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  3\n",
      "min_samples_leaf:  5\n",
      "Random state is:  255\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
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      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.004925836344787676\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  5\n",
      "min_samples_leaf:  3\n",
      "Random state is:  0\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
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      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.006657512925537517\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  5\n",
      "min_samples_leaf:  3\n",
      "Random state is:  42\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
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      "Success: True\n",
      "\n",
      "average reward: 0.006906254698669535\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  5\n",
      "min_samples_leaf:  3\n",
      "Random state is:  255\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
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      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.008250746138500149\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  5\n",
      "min_samples_leaf:  4\n",
      "Random state is:  0\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
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      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.007335968341395068\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  5\n",
      "min_samples_leaf:  4\n",
      "Random state is:  42\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
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      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.007693178813820476\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  5\n",
      "min_samples_leaf:  4\n",
      "Random state is:  255\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
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      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.005883910405842172\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  5\n",
      "min_samples_leaf:  5\n",
      "Random state is:  0\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
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      "Success: False\n",
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      "Success: True\n",
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      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.005629879721236851\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  5\n",
      "min_samples_leaf:  5\n",
      "Random state is:  42\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
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      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: False\n",
      "\n",
      "average reward: 0.006182810954052802\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  5\n",
      "min_samples_leaf:  5\n",
      "Random state is:  255\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
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      "Run 7 / 10\n",
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      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.006783744766376995\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  7\n",
      "min_samples_leaf:  3\n",
      "Random state is:  0\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
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      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: False\n",
      "\n",
      "average reward: 0.004470711701493918\n",
      "reached target: 4 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  7\n",
      "min_samples_leaf:  3\n",
      "Random state is:  42\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.007896895827426968\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  7\n",
      "min_samples_leaf:  3\n",
      "Random state is:  255\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
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      "Run 6 / 10\n",
      "Success: True\n",
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      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.00869238244483492\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  7\n",
      "min_samples_leaf:  4\n",
      "Random state is:  0\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
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      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: False\n",
      "\n",
      "average reward: 0.005430428932863141\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  7\n",
      "min_samples_leaf:  4\n",
      "Random state is:  42\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
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      "Success: False\n",
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      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.008692211735846084\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  7\n",
      "min_samples_leaf:  4\n",
      "Random state is:  255\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
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      "Run 4 / 10\n",
      "Success: False\n",
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      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.007863335098502629\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  7\n",
      "min_samples_leaf:  5\n",
      "Random state is:  0\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.008587295737556034\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  7\n",
      "min_samples_leaf:  5\n",
      "Random state is:  42\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: False\n",
      "\n",
      "average reward: 0.006270881138976241\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "--------------------------------------------------------\n",
      "The size of the kernel for dilating edges:  7\n",
      "min_samples_leaf:  5\n",
      "Random state is:  255\n",
      "--------------------------------------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
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      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.0067901719633381725\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_parameters = {'dilating_edges': [3, 5, 7], 'min_samples_leaf': [3, 4, 5]}\n",
    "\n",
    "def rf_policy(observation):    \n",
    "        feature_img = feat_extract(observation, dilate_kernal=test_parameters['dilating_edges'][i])\n",
    "        action = rf_model.predict(feature_img.reshape(1, -1))\n",
    "        return action[0]\n",
    "\n",
    "for i in range(3):\n",
    "    for j in range(3):\n",
    "        for r in [0, 42, 255]:\n",
    "            filename = 'pkl/rf_model_3-2_' + str(r) + '_' + str(test_parameters['dilating_edges'][i]) + '_'  + str(test_parameters['min_samples_leaf'][j]) + '.pkl'\n",
    "            with open(filename, 'rb') as f:\n",
    "                rf_model = pickle.load(f)\n",
    "    \n",
    "            print(\"--------------------------------------------------------\")\n",
    "            print(\"The size of the kernel for dilating edges: \", test_parameters['dilating_edges'][i])\n",
    "            print(\"min_samples_leaf: \", test_parameters['min_samples_leaf'][j])\n",
    "            print(\"Random state is: \", r)\n",
    "            print(\"--------------------------------------------------------\")\n",
    "            \n",
    "            num_completed, avg_reward = test_policy(rf_policy, envs_norender[1])\n",
    "            print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0252446f-c5c7-4448-b71e-bf7dcf224fc0",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 4. Enabling Generalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "368aa419-c0a6-47d2-9a04-7b6d18b2b701",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Timeid 3')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(observations[100])\n",
    "plt.title('Timeid 1')\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(X[100].reshape(60,80))\n",
    "plt.title('Timeid 1')\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(observations[2000])\n",
    "plt.title('Timeid 2')\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(X[2000].reshape(60,80))\n",
    "plt.title('Timeid 2')\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(observations[3000])\n",
    "plt.title('Timeid 3')\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(X[3000].reshape(60,80))\n",
    "plt.title('Timeid 3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3fa2ef56-faae-424b-a146-01d0778dc087",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_id0 = X[np.where(time_ids==0)]\n",
    "y_id0 = y[np.where(time_ids==0)]\n",
    "X_id1 = X[np.where(time_ids==1)]\n",
    "y_id1 = y[np.where(time_ids==1)]\n",
    "X_id01 = np.concatenate([X_id0,X_id1],axis=0)\n",
    "y_id01 = np.concatenate([y_id0,y_id1],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "44d5f6d8-87dc-4f1d-98d9-7494c5729f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model based on Timeid1\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X_id1, y_id1, test_size=0.2, random_state=42)\n",
    "# X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n",
    "\n",
    "# print('Training dataset size:', X_train.shape)\n",
    "# print('Evluation dataset size:', X_val.shape)\n",
    "# print('Test dataset size:', X_test.shape)\n",
    "\n",
    "# rf1_model = RandomForestClassifier(n_estimators=100, min_samples_leaf=4, random_state=42)\n",
    "# rf1_model.fit(X_train, y_train)\n",
    "# filename = 'pkl/rf_model4-1.pkl'\n",
    "# with open(filename, 'wb') as f:\n",
    "#     pickle.dump(rf1_model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f42d0cf1-de1a-4eef-9af8-4821214171a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n",
      "Random state is:  0\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: False\n",
      "Run 6 / 10\n",
      "Success: False\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.00635059377870479\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "Random state is:  42\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.00679281697237525\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "Random state is:  255\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: False\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.004834345713372458\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Use Timeid2 to evaluate generalization\n",
    "def rf1_policy(observation):    \n",
    "    feature_img = feat_extract(observation)\n",
    "    action = rf1_model.predict(feature_img.reshape(1, -1))\n",
    "    return action[0]\n",
    "\n",
    "for r in [0, 42, 255]:\n",
    "    filename = 'pkl/rf_model_3-1_' + str(r) + '.pkl'\n",
    "    with open(filename, 'rb') as f:\n",
    "        rf1_model=pickle.load(f)\n",
    "    print(\"----------------------------\")\n",
    "    print(\"Random state is: \", r)\n",
    "    print(\"----------------------------\")\n",
    "    num_completed, avg_reward = test_policy(rf1_policy, envs_norender[2])\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2816a8d1-65c3-40f3-a1c7-d4b973a29a5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model based on Timeid0 & Timeid1\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X_id01, y_id01, test_size=0.2, random_state=42)\n",
    "# X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n",
    "\n",
    "# print('Training dataset size:', X_train.shape)\n",
    "# print('Evluation dataset size:', X_val.shape)\n",
    "# print('Test dataset size:', X_test.shape)\n",
    "\n",
    "# for r in [0, 42, 255]:\n",
    "#     rf01_model = RandomForestClassifier(n_estimators=100, min_samples_leaf=4, random_state=r)\n",
    "#     rf01_model.fit(X_train, y_train)\n",
    "#     filename = 'pkl/rf_model_4_time=0+1_' + str(r) + '.pkl'\n",
    "#     with open(filename, 'wb') as f:\n",
    "#         pickle.dump(rf01_model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "48cd4cb4-fe50-498d-b972-53d4cfc2c3c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n",
      "Random state is:  0\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: False\n",
      "\n",
      "average reward: 0.005417960124134664\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "Random state is:  42\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.00787121820564967\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "Random state is:  255\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.005320344321045457\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Use Timeid2 to evaluate generalization\n",
    "def rf01_policy(observation):    \n",
    "    feature_img = feat_extract(observation)\n",
    "    action = rf01_model.predict(feature_img.reshape(1, -1))\n",
    "    return action[0]\n",
    "for r in [0, 42, 255]:\n",
    "    filename = 'pkl/rf_model_4_time=0+1_' + str(r) + '.pkl'\n",
    "    with open(filename, 'rb') as f:\n",
    "        rf01_model=pickle.load(f)\n",
    "    print(\"----------------------------\")\n",
    "    print(\"Random state is: \", r)\n",
    "    print(\"----------------------------\")\n",
    "    num_completed, avg_reward = test_policy(rf01_policy, envs_norender[2])\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "afa268f4-5063-46bb-9dbe-6d2af86de61e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_parameters = {'min_samples_leaf': [4, 6, 8]}\n",
    "\n",
    "\n",
    "# for i in range(3):\n",
    "    \n",
    "#     X_train, X_test, y_train, y_test = train_test_split(X_id01, y_id01, test_size=0.2, random_state=42)\n",
    "#     X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n",
    "        \n",
    "        \n",
    "#     for r in [0, 42, 255]:\n",
    "    \n",
    "#         rf01_model = RandomForestClassifier(random_state=r, min_samples_leaf=test_parameters['min_samples_leaf'][i])\n",
    "#         rf01_model.fit(X_train, y_train)\n",
    "#         filename = 'pkl/rf_model_4_time=0+1_' + str(r) + '_' + str(test_parameters['min_samples_leaf'][i]) + '.pkl'\n",
    "#         with open(filename, 'wb') as f:\n",
    "#             pickle.dump(rf01_model, f)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fc110beb-76ae-4b5e-a8d6-cb884654a655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n",
      "min_samples_leaf:  4\n",
      "Random state is:  0\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: False\n",
      "\n",
      "average reward: 0.005417960124134664\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "min_samples_leaf:  4\n",
      "Random state is:  42\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.00787121820564967\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "min_samples_leaf:  4\n",
      "Random state is:  255\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.005320344321045457\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "min_samples_leaf:  6\n",
      "Random state is:  0\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.005357316690423597\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "min_samples_leaf:  6\n",
      "Random state is:  42\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: False\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: False\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.0037011400020480383\n",
      "reached target: 5 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "min_samples_leaf:  6\n",
      "Random state is:  255\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.006072227399979816\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "min_samples_leaf:  8\n",
      "Random state is:  0\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.006011363185349628\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "min_samples_leaf:  8\n",
      "Random state is:  42\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: False\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.004915522204828251\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "----------------------------\n",
      "min_samples_leaf:  8\n",
      "Random state is:  255\n",
      "----------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: False\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.005353519817860994\n",
      "reached target: 7 / 10\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_parameters = {'min_samples_leaf': [4, 6, 8]}\n",
    "\n",
    "def rf01_policy(observation):    \n",
    "    feature_img = feat_extract(observation)\n",
    "    action = rf01_model.predict(feature_img.reshape(1, -1))\n",
    "    return action[0]\n",
    "for i in range(3):\n",
    "    for r in [0, 42, 255]:\n",
    "        filename = 'pkl/rf_model_4_time=0+1_' + str(r) + '_' + str(test_parameters['min_samples_leaf'][i]) + '.pkl'\n",
    "        with open(filename, 'rb') as f:\n",
    "            rf01_model = pickle.load(f)\n",
    "        print(\"----------------------------\")\n",
    "        print(\"min_samples_leaf: \", test_parameters['min_samples_leaf'][i])\n",
    "        print('Random state is: ', r)\n",
    "        print(\"----------------------------\")\n",
    "\n",
    "        num_completed, avg_reward = test_policy(rf01_policy, envs_norender[2])\n",
    "        print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "12f62e28-be49-4b0e-b887-f99e65319f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "# X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n",
    "\n",
    "# print('Training dataset size:', X_train.shape)\n",
    "# print('Evluation dataset size:', X_val.shape)\n",
    "# print('Test dataset size:', X_test.shape)\n",
    "# for r in [0, 42, 255]:\n",
    "#     rf012_model = RandomForestClassifier(n_estimators=100, min_samples_leaf=8, random_state=r)\n",
    "#     rf012_model.fit(X_train, y_train)\n",
    "#     filename = 'pkl/rf_model_4_time=0+1+2_' + str(r) + '.pkl'\n",
    "#     with open(filename, 'wb') as f:\n",
    "#         pickle.dump(rf012_model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4e22468a-dc39-4120-a40c-68557d78bb03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------\n",
      "time_id is:  0\n",
      "Random state is:  0\n",
      "-------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: False\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.006456127128542639\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "-------------------------\n",
      "time_id is:  1\n",
      "Random state is:  0\n",
      "-------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.00909905256594803\n",
      "reached target: 9 / 10\n",
      "\n",
      "\n",
      "-------------------------\n",
      "time_id is:  0\n",
      "Random state is:  42\n",
      "-------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: False\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.006517964674206472\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "-------------------------\n",
      "time_id is:  1\n",
      "Random state is:  42\n",
      "-------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.008050759516226528\n",
      "reached target: 9 / 10\n",
      "\n",
      "\n",
      "-------------------------\n",
      "time_id is:  0\n",
      "Random state is:  255\n",
      "-------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: False\n",
      "Run 5 / 10\n",
      "Success: False\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: False\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.006418409818687508\n",
      "reached target: 6 / 10\n",
      "\n",
      "\n",
      "-------------------------\n",
      "time_id is:  1\n",
      "Random state is:  255\n",
      "-------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: False\n",
      "\n",
      "average reward: 0.008127223045061633\n",
      "reached target: 8 / 10\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# use time_ids 0, 1 to validate\n",
    "import pickle\n",
    "def rf012_policy(observation):    \n",
    "    feature_img = feat_extract(observation)\n",
    "    action = rf012_model.predict(feature_img.reshape(1, -1))\n",
    "    return action[0]\n",
    "for r in [0, 42, 255]:\n",
    "    filename = 'pkl/rf_model_4_time=0+1+2_' + str(r) + '.pkl'\n",
    "    with open(filename, 'rb') as f:\n",
    "        rf012_model=pickle.load(f)\n",
    "    for i in range(2):\n",
    "        print(\"-------------------------\")\n",
    "        print(\"time_id is: \", i)\n",
    "        print(\"Random state is: \", r)\n",
    "        print(\"-------------------------\")\n",
    "        num_completed, avg_reward = test_policy(rf012_policy, envs_norender[i])\n",
    "        print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9120bdbd-eb6c-4ff0-8701-d67d5fc48528",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------\n",
      "Random state is:  0\n",
      "-------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.008824392043085638\n",
      "reached target: 9 / 10\n",
      "\n",
      "\n",
      "-------------------------\n",
      "Random state is:  42\n",
      "-------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.0091442200338973\n",
      "reached target: 9 / 10\n",
      "\n",
      "\n",
      "-------------------------\n",
      "Random state is:  255\n",
      "-------------------------\n",
      "Run 1 / 10\n",
      "Success: False\n",
      "Run 2 / 10\n",
      "Success: True\n",
      "Run 3 / 10\n",
      "Success: True\n",
      "Run 4 / 10\n",
      "Success: True\n",
      "Run 5 / 10\n",
      "Success: True\n",
      "Run 6 / 10\n",
      "Success: True\n",
      "Run 7 / 10\n",
      "Success: True\n",
      "Run 8 / 10\n",
      "Success: True\n",
      "Run 9 / 10\n",
      "Success: True\n",
      "Run 10 / 10\n",
      "Success: True\n",
      "\n",
      "average reward: 0.008781439762590855\n",
      "reached target: 9 / 10\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Use Timeid3 to test\n",
    "def rf012_policy(observation):    \n",
    "    feature_img = feat_extract(observation)\n",
    "    action = rf012_model.predict(feature_img.reshape(1, -1))\n",
    "    return action[0]\n",
    "for r in [0, 42, 255]:\n",
    "    filename = 'pkl/rf_model_4_time=0+1+2_' + str(r) + '.pkl'\n",
    "    with open(filename, 'rb') as f:\n",
    "        rf012_model=pickle.load(f)\n",
    "    print(\"-------------------------\")\n",
    "    print(\"Random state is: \", r)\n",
    "    print(\"-------------------------\")\n",
    "    num_completed, avg_reward = test_policy(rf012_policy, envs_norender[3])\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "637a8954-a70a-43f0-828b-9562ea4d9244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running time is:  11.867929450670879  mins\n"
     ]
    }
   ],
   "source": [
    "end = time.time()\n",
    "print(\"running time is: \", (end-start)/60, \" mins\")"
   ]
  }
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